{"title":"通过神经模糊逻辑优化电动汽车路由中途充电站的选择","authors":"S. Priya;R. Radha;P. Anandha Prakash;R. Nandhini","doi":"10.1109/ACCESS.2024.3468471","DOIUrl":null,"url":null,"abstract":"We propose a comprehensive Electric Vehicle (EV) routing algorithm to find the optimal set of intermediate charging stations (CSs) present between a given source and destination. Each intermediate charging station is selected to maximize efficiency by considering three crucial parameters: distance to reach the destination from the selected CS, waiting time at the CS, and energy consumed to reach the selected CS along the route. Unlike existing algorithms, that focus solely on energy or distance, this algorithm integrates all three factors to generate an efficient path. Machine Learning (ML) is employed to predict vehicle range using data provided by the user, ensuring that the selected route avoids the risk of battery depletion midway. This predicted range is then used to determine CSs that can be reached from current location. Furthermore, the algorithm utilizes Breadth-First Search (BFS) to identify CS nodes with the least cost, enhancing routing accuracy. The cost of reaching each CS node is calculated using Neuro-Fuzzy Logic, which effectively handles uncertain inputs, which is common in EV routing scenarios. Comparative analysis against a recently proposed route planning algorithm (EV-RPA) reveals superior performance of the proposed approach, particularly as the number of CSs increases. It excels in all three aspects: distance covered, waiting time, and energy consumed, highlighting its effectiveness in optimizing EV routing.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695073","citationCount":"0","resultStr":"{\"title\":\"Optimizing the Selection of Intermediate Charging Stations in EV Routing Through Neuro-Fuzzy Logic\",\"authors\":\"S. Priya;R. Radha;P. Anandha Prakash;R. Nandhini\",\"doi\":\"10.1109/ACCESS.2024.3468471\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a comprehensive Electric Vehicle (EV) routing algorithm to find the optimal set of intermediate charging stations (CSs) present between a given source and destination. Each intermediate charging station is selected to maximize efficiency by considering three crucial parameters: distance to reach the destination from the selected CS, waiting time at the CS, and energy consumed to reach the selected CS along the route. Unlike existing algorithms, that focus solely on energy or distance, this algorithm integrates all three factors to generate an efficient path. Machine Learning (ML) is employed to predict vehicle range using data provided by the user, ensuring that the selected route avoids the risk of battery depletion midway. This predicted range is then used to determine CSs that can be reached from current location. Furthermore, the algorithm utilizes Breadth-First Search (BFS) to identify CS nodes with the least cost, enhancing routing accuracy. The cost of reaching each CS node is calculated using Neuro-Fuzzy Logic, which effectively handles uncertain inputs, which is common in EV routing scenarios. Comparative analysis against a recently proposed route planning algorithm (EV-RPA) reveals superior performance of the proposed approach, particularly as the number of CSs increases. It excels in all three aspects: distance covered, waiting time, and energy consumed, highlighting its effectiveness in optimizing EV routing.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695073\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10695073/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10695073/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Optimizing the Selection of Intermediate Charging Stations in EV Routing Through Neuro-Fuzzy Logic
We propose a comprehensive Electric Vehicle (EV) routing algorithm to find the optimal set of intermediate charging stations (CSs) present between a given source and destination. Each intermediate charging station is selected to maximize efficiency by considering three crucial parameters: distance to reach the destination from the selected CS, waiting time at the CS, and energy consumed to reach the selected CS along the route. Unlike existing algorithms, that focus solely on energy or distance, this algorithm integrates all three factors to generate an efficient path. Machine Learning (ML) is employed to predict vehicle range using data provided by the user, ensuring that the selected route avoids the risk of battery depletion midway. This predicted range is then used to determine CSs that can be reached from current location. Furthermore, the algorithm utilizes Breadth-First Search (BFS) to identify CS nodes with the least cost, enhancing routing accuracy. The cost of reaching each CS node is calculated using Neuro-Fuzzy Logic, which effectively handles uncertain inputs, which is common in EV routing scenarios. Comparative analysis against a recently proposed route planning algorithm (EV-RPA) reveals superior performance of the proposed approach, particularly as the number of CSs increases. It excels in all three aspects: distance covered, waiting time, and energy consumed, highlighting its effectiveness in optimizing EV routing.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.